mirror of
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721 lines
28 KiB
Python
721 lines
28 KiB
Python
"""functools.py - Tools for working with functions and callable objects
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"""
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# Python module wrapper for _functools C module
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# to allow utilities written in Python to be added
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# to the functools module.
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# Written by Nick Coghlan <ncoghlan at gmail.com>,
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# Raymond Hettinger <python at rcn.com>,
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# and Łukasz Langa <lukasz at langa.pl>.
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# Copyright (C) 2006-2013 Python Software Foundation.
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# See C source code for _functools credits/copyright
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__all__ = ['update_wrapper', 'wraps', 'WRAPPER_ASSIGNMENTS', 'WRAPPER_UPDATES',
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'total_ordering', 'cmp_to_key', 'lru_cache', 'reduce', 'partial',
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'partialmethod', 'singledispatch']
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try:
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from _functools import reduce
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except ImportError:
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pass
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from abc import get_cache_token
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from collections import namedtuple
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from types import MappingProxyType
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from weakref import WeakKeyDictionary
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try:
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from _thread import RLock
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except:
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class RLock:
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'Dummy reentrant lock for builds without threads'
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def __enter__(self): pass
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def __exit__(self, exctype, excinst, exctb): pass
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################################################################################
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### update_wrapper() and wraps() decorator
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################################################################################
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# update_wrapper() and wraps() are tools to help write
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# wrapper functions that can handle naive introspection
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WRAPPER_ASSIGNMENTS = ('__module__', '__name__', '__qualname__', '__doc__',
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'__annotations__')
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WRAPPER_UPDATES = ('__dict__',)
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def update_wrapper(wrapper,
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wrapped,
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assigned = WRAPPER_ASSIGNMENTS,
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updated = WRAPPER_UPDATES):
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"""Update a wrapper function to look like the wrapped function
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wrapper is the function to be updated
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wrapped is the original function
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assigned is a tuple naming the attributes assigned directly
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from the wrapped function to the wrapper function (defaults to
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functools.WRAPPER_ASSIGNMENTS)
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updated is a tuple naming the attributes of the wrapper that
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are updated with the corresponding attribute from the wrapped
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function (defaults to functools.WRAPPER_UPDATES)
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"""
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for attr in assigned:
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try:
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value = getattr(wrapped, attr)
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except AttributeError:
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pass
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else:
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setattr(wrapper, attr, value)
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for attr in updated:
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getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
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# Issue #17482: set __wrapped__ last so we don't inadvertently copy it
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# from the wrapped function when updating __dict__
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wrapper.__wrapped__ = wrapped
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# Return the wrapper so this can be used as a decorator via partial()
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return wrapper
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def wraps(wrapped,
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assigned = WRAPPER_ASSIGNMENTS,
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updated = WRAPPER_UPDATES):
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"""Decorator factory to apply update_wrapper() to a wrapper function
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Returns a decorator that invokes update_wrapper() with the decorated
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function as the wrapper argument and the arguments to wraps() as the
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remaining arguments. Default arguments are as for update_wrapper().
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This is a convenience function to simplify applying partial() to
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update_wrapper().
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"""
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return partial(update_wrapper, wrapped=wrapped,
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assigned=assigned, updated=updated)
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################################################################################
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### total_ordering class decorator
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################################################################################
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# The correct way to indicate that a comparison operation doesn't
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# recognise the other type is to return NotImplemented and let the
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# interpreter handle raising TypeError if both operands return
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# NotImplemented from their respective comparison methods
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#
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# This makes the implementation of total_ordering more complicated, since
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# we need to be careful not to trigger infinite recursion when two
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# different types that both use this decorator encounter each other.
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#
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# For example, if a type implements __lt__, it's natural to define
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# __gt__ as something like:
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#
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# lambda self, other: not self < other and not self == other
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#
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# However, using the operator syntax like that ends up invoking the full
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# type checking machinery again and means we can end up bouncing back and
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# forth between the two operands until we run out of stack space.
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#
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# The solution is to define helper functions that invoke the appropriate
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# magic methods directly, ensuring we only try each operand once, and
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# return NotImplemented immediately if it is returned from the
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# underlying user provided method. Using this scheme, the __gt__ derived
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# from a user provided __lt__ becomes:
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#
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# lambda self, other: _not_op_and_not_eq(self.__lt__, self, other))
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def _not_op(op, other):
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# "not a < b" handles "a >= b"
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# "not a <= b" handles "a > b"
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# "not a >= b" handles "a < b"
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# "not a > b" handles "a <= b"
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op_result = op(other)
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if op_result is NotImplemented:
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return NotImplemented
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return not op_result
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def _op_or_eq(op, self, other):
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# "a < b or a == b" handles "a <= b"
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# "a > b or a == b" handles "a >= b"
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op_result = op(other)
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if op_result is NotImplemented:
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return NotImplemented
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return op_result or self == other
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def _not_op_and_not_eq(op, self, other):
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# "not (a < b or a == b)" handles "a > b"
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# "not a < b and a != b" is equivalent
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# "not (a > b or a == b)" handles "a < b"
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# "not a > b and a != b" is equivalent
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op_result = op(other)
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if op_result is NotImplemented:
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return NotImplemented
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return not op_result and self != other
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def _not_op_or_eq(op, self, other):
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# "not a <= b or a == b" handles "a >= b"
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# "not a >= b or a == b" handles "a <= b"
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op_result = op(other)
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if op_result is NotImplemented:
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return NotImplemented
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return not op_result or self == other
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def _op_and_not_eq(op, self, other):
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# "a <= b and not a == b" handles "a < b"
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# "a >= b and not a == b" handles "a > b"
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op_result = op(other)
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if op_result is NotImplemented:
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return NotImplemented
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return op_result and self != other
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def total_ordering(cls):
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"""Class decorator that fills in missing ordering methods"""
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convert = {
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'__lt__': [('__gt__', lambda self, other: _not_op_and_not_eq(self.__lt__, self, other)),
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('__le__', lambda self, other: _op_or_eq(self.__lt__, self, other)),
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('__ge__', lambda self, other: _not_op(self.__lt__, other))],
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'__le__': [('__ge__', lambda self, other: _not_op_or_eq(self.__le__, self, other)),
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('__lt__', lambda self, other: _op_and_not_eq(self.__le__, self, other)),
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('__gt__', lambda self, other: _not_op(self.__le__, other))],
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'__gt__': [('__lt__', lambda self, other: _not_op_and_not_eq(self.__gt__, self, other)),
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('__ge__', lambda self, other: _op_or_eq(self.__gt__, self, other)),
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('__le__', lambda self, other: _not_op(self.__gt__, other))],
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'__ge__': [('__le__', lambda self, other: _not_op_or_eq(self.__ge__, self, other)),
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('__gt__', lambda self, other: _op_and_not_eq(self.__ge__, self, other)),
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('__lt__', lambda self, other: _not_op(self.__ge__, other))]
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}
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# Find user-defined comparisons (not those inherited from object).
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roots = [op for op in convert if getattr(cls, op, None) is not getattr(object, op, None)]
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if not roots:
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raise ValueError('must define at least one ordering operation: < > <= >=')
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root = max(roots) # prefer __lt__ to __le__ to __gt__ to __ge__
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for opname, opfunc in convert[root]:
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if opname not in roots:
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opfunc.__name__ = opname
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opfunc.__doc__ = getattr(int, opname).__doc__
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setattr(cls, opname, opfunc)
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return cls
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################################################################################
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### cmp_to_key() function converter
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################################################################################
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def cmp_to_key(mycmp):
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"""Convert a cmp= function into a key= function"""
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class K(object):
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__slots__ = ['obj']
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def __init__(self, obj):
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self.obj = obj
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def __lt__(self, other):
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return mycmp(self.obj, other.obj) < 0
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def __gt__(self, other):
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return mycmp(self.obj, other.obj) > 0
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def __eq__(self, other):
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return mycmp(self.obj, other.obj) == 0
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def __le__(self, other):
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return mycmp(self.obj, other.obj) <= 0
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def __ge__(self, other):
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return mycmp(self.obj, other.obj) >= 0
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def __ne__(self, other):
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return mycmp(self.obj, other.obj) != 0
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__hash__ = None
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return K
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try:
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from _functools import cmp_to_key
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except ImportError:
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pass
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################################################################################
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### partial() argument application
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################################################################################
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# Purely functional, no descriptor behaviour
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def partial(func, *args, **keywords):
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"""New function with partial application of the given arguments
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and keywords.
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"""
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def newfunc(*fargs, **fkeywords):
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newkeywords = keywords.copy()
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newkeywords.update(fkeywords)
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return func(*(args + fargs), **newkeywords)
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newfunc.func = func
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newfunc.args = args
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newfunc.keywords = keywords
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return newfunc
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try:
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from _functools import partial
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except ImportError:
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pass
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# Descriptor version
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class partialmethod(object):
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"""Method descriptor with partial application of the given arguments
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and keywords.
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Supports wrapping existing descriptors and handles non-descriptor
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callables as instance methods.
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"""
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def __init__(self, func, *args, **keywords):
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if not callable(func) and not hasattr(func, "__get__"):
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raise TypeError("{!r} is not callable or a descriptor"
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.format(func))
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# func could be a descriptor like classmethod which isn't callable,
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# so we can't inherit from partial (it verifies func is callable)
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if isinstance(func, partialmethod):
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# flattening is mandatory in order to place cls/self before all
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# other arguments
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# it's also more efficient since only one function will be called
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self.func = func.func
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self.args = func.args + args
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self.keywords = func.keywords.copy()
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self.keywords.update(keywords)
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else:
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self.func = func
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self.args = args
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self.keywords = keywords
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def __repr__(self):
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args = ", ".join(map(repr, self.args))
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keywords = ", ".join("{}={!r}".format(k, v)
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for k, v in self.keywords.items())
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format_string = "{module}.{cls}({func}, {args}, {keywords})"
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return format_string.format(module=self.__class__.__module__,
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cls=self.__class__.__name__,
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func=self.func,
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args=args,
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keywords=keywords)
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def _make_unbound_method(self):
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def _method(*args, **keywords):
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call_keywords = self.keywords.copy()
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call_keywords.update(keywords)
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cls_or_self, *rest = args
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call_args = (cls_or_self,) + self.args + tuple(rest)
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return self.func(*call_args, **call_keywords)
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_method.__isabstractmethod__ = self.__isabstractmethod__
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_method._partialmethod = self
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return _method
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def __get__(self, obj, cls):
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get = getattr(self.func, "__get__", None)
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result = None
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if get is not None:
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new_func = get(obj, cls)
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if new_func is not self.func:
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# Assume __get__ returning something new indicates the
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# creation of an appropriate callable
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result = partial(new_func, *self.args, **self.keywords)
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try:
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result.__self__ = new_func.__self__
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except AttributeError:
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pass
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if result is None:
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# If the underlying descriptor didn't do anything, treat this
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# like an instance method
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result = self._make_unbound_method().__get__(obj, cls)
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return result
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@property
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def __isabstractmethod__(self):
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return getattr(self.func, "__isabstractmethod__", False)
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################################################################################
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### LRU Cache function decorator
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################################################################################
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_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])
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class _HashedSeq(list):
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""" This class guarantees that hash() will be called no more than once
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per element. This is important because the lru_cache() will hash
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the key multiple times on a cache miss.
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"""
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__slots__ = 'hashvalue'
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def __init__(self, tup, hash=hash):
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self[:] = tup
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self.hashvalue = hash(tup)
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def __hash__(self):
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return self.hashvalue
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def _make_key(args, kwds, typed,
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kwd_mark = (object(),),
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fasttypes = {int, str, frozenset, type(None)},
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sorted=sorted, tuple=tuple, type=type, len=len):
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"""Make a cache key from optionally typed positional and keyword arguments
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The key is constructed in a way that is flat as possible rather than
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as a nested structure that would take more memory.
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If there is only a single argument and its data type is known to cache
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its hash value, then that argument is returned without a wrapper. This
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saves space and improves lookup speed.
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"""
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key = args
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if kwds:
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sorted_items = sorted(kwds.items())
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key += kwd_mark
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for item in sorted_items:
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key += item
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if typed:
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key += tuple(type(v) for v in args)
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if kwds:
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key += tuple(type(v) for k, v in sorted_items)
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elif len(key) == 1 and type(key[0]) in fasttypes:
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return key[0]
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return _HashedSeq(key)
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def lru_cache(maxsize=128, typed=False):
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"""Least-recently-used cache decorator.
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If *maxsize* is set to None, the LRU features are disabled and the cache
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can grow without bound.
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If *typed* is True, arguments of different types will be cached separately.
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For example, f(3.0) and f(3) will be treated as distinct calls with
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distinct results.
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Arguments to the cached function must be hashable.
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View the cache statistics named tuple (hits, misses, maxsize, currsize)
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with f.cache_info(). Clear the cache and statistics with f.cache_clear().
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Access the underlying function with f.__wrapped__.
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See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
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"""
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# Users should only access the lru_cache through its public API:
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# cache_info, cache_clear, and f.__wrapped__
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# The internals of the lru_cache are encapsulated for thread safety and
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# to allow the implementation to change (including a possible C version).
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# Early detection of an erroneous call to @lru_cache without any arguments
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# resulting in the inner function being passed to maxsize instead of an
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# integer or None.
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if maxsize is not None and not isinstance(maxsize, int):
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raise TypeError('Expected maxsize to be an integer or None')
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# Constants shared by all lru cache instances:
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sentinel = object() # unique object used to signal cache misses
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make_key = _make_key # build a key from the function arguments
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PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields
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def decorating_function(user_function):
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cache = {}
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hits = misses = 0
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full = False
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cache_get = cache.get # bound method to lookup a key or return None
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lock = RLock() # because linkedlist updates aren't threadsafe
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root = [] # root of the circular doubly linked list
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root[:] = [root, root, None, None] # initialize by pointing to self
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if maxsize == 0:
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def wrapper(*args, **kwds):
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# No caching -- just a statistics update after a successful call
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nonlocal misses
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result = user_function(*args, **kwds)
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misses += 1
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return result
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|
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elif maxsize is None:
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def wrapper(*args, **kwds):
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# Simple caching without ordering or size limit
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nonlocal hits, misses
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key = make_key(args, kwds, typed)
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result = cache_get(key, sentinel)
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if result is not sentinel:
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hits += 1
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return result
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result = user_function(*args, **kwds)
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cache[key] = result
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misses += 1
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return result
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else:
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def wrapper(*args, **kwds):
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# Size limited caching that tracks accesses by recency
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nonlocal root, hits, misses, full
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key = make_key(args, kwds, typed)
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with lock:
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link = cache_get(key)
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if link is not None:
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# Move the link to the front of the circular queue
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link_prev, link_next, _key, result = link
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link_prev[NEXT] = link_next
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link_next[PREV] = link_prev
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last = root[PREV]
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last[NEXT] = root[PREV] = link
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link[PREV] = last
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link[NEXT] = root
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hits += 1
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return result
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result = user_function(*args, **kwds)
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with lock:
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if key in cache:
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# Getting here means that this same key was added to the
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# cache while the lock was released. Since the link
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# update is already done, we need only return the
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# computed result and update the count of misses.
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pass
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elif full:
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# Use the old root to store the new key and result.
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oldroot = root
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oldroot[KEY] = key
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oldroot[RESULT] = result
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# Empty the oldest link and make it the new root.
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# Keep a reference to the old key and old result to
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# prevent their ref counts from going to zero during the
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# update. That will prevent potentially arbitrary object
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# clean-up code (i.e. __del__) from running while we're
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# still adjusting the links.
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root = oldroot[NEXT]
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oldkey = root[KEY]
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oldresult = root[RESULT]
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root[KEY] = root[RESULT] = None
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# Now update the cache dictionary.
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del cache[oldkey]
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# Save the potentially reentrant cache[key] assignment
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# for last, after the root and links have been put in
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# a consistent state.
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cache[key] = oldroot
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else:
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# Put result in a new link at the front of the queue.
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last = root[PREV]
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link = [last, root, key, result]
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last[NEXT] = root[PREV] = cache[key] = link
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full = (len(cache) >= maxsize)
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misses += 1
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return result
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|
|
def cache_info():
|
|
"""Report cache statistics"""
|
|
with lock:
|
|
return _CacheInfo(hits, misses, maxsize, len(cache))
|
|
|
|
def cache_clear():
|
|
"""Clear the cache and cache statistics"""
|
|
nonlocal hits, misses, full
|
|
with lock:
|
|
cache.clear()
|
|
root[:] = [root, root, None, None]
|
|
hits = misses = 0
|
|
full = False
|
|
|
|
wrapper.cache_info = cache_info
|
|
wrapper.cache_clear = cache_clear
|
|
return update_wrapper(wrapper, user_function)
|
|
|
|
return decorating_function
|
|
|
|
|
|
################################################################################
|
|
### singledispatch() - single-dispatch generic function decorator
|
|
################################################################################
|
|
|
|
def _c3_merge(sequences):
|
|
"""Merges MROs in *sequences* to a single MRO using the C3 algorithm.
|
|
|
|
Adapted from http://www.python.org/download/releases/2.3/mro/.
|
|
|
|
"""
|
|
result = []
|
|
while True:
|
|
sequences = [s for s in sequences if s] # purge empty sequences
|
|
if not sequences:
|
|
return result
|
|
for s1 in sequences: # find merge candidates among seq heads
|
|
candidate = s1[0]
|
|
for s2 in sequences:
|
|
if candidate in s2[1:]:
|
|
candidate = None
|
|
break # reject the current head, it appears later
|
|
else:
|
|
break
|
|
if not candidate:
|
|
raise RuntimeError("Inconsistent hierarchy")
|
|
result.append(candidate)
|
|
# remove the chosen candidate
|
|
for seq in sequences:
|
|
if seq[0] == candidate:
|
|
del seq[0]
|
|
|
|
def _c3_mro(cls, abcs=None):
|
|
"""Computes the method resolution order using extended C3 linearization.
|
|
|
|
If no *abcs* are given, the algorithm works exactly like the built-in C3
|
|
linearization used for method resolution.
|
|
|
|
If given, *abcs* is a list of abstract base classes that should be inserted
|
|
into the resulting MRO. Unrelated ABCs are ignored and don't end up in the
|
|
result. The algorithm inserts ABCs where their functionality is introduced,
|
|
i.e. issubclass(cls, abc) returns True for the class itself but returns
|
|
False for all its direct base classes. Implicit ABCs for a given class
|
|
(either registered or inferred from the presence of a special method like
|
|
__len__) are inserted directly after the last ABC explicitly listed in the
|
|
MRO of said class. If two implicit ABCs end up next to each other in the
|
|
resulting MRO, their ordering depends on the order of types in *abcs*.
|
|
|
|
"""
|
|
for i, base in enumerate(reversed(cls.__bases__)):
|
|
if hasattr(base, '__abstractmethods__'):
|
|
boundary = len(cls.__bases__) - i
|
|
break # Bases up to the last explicit ABC are considered first.
|
|
else:
|
|
boundary = 0
|
|
abcs = list(abcs) if abcs else []
|
|
explicit_bases = list(cls.__bases__[:boundary])
|
|
abstract_bases = []
|
|
other_bases = list(cls.__bases__[boundary:])
|
|
for base in abcs:
|
|
if issubclass(cls, base) and not any(
|
|
issubclass(b, base) for b in cls.__bases__
|
|
):
|
|
# If *cls* is the class that introduces behaviour described by
|
|
# an ABC *base*, insert said ABC to its MRO.
|
|
abstract_bases.append(base)
|
|
for base in abstract_bases:
|
|
abcs.remove(base)
|
|
explicit_c3_mros = [_c3_mro(base, abcs=abcs) for base in explicit_bases]
|
|
abstract_c3_mros = [_c3_mro(base, abcs=abcs) for base in abstract_bases]
|
|
other_c3_mros = [_c3_mro(base, abcs=abcs) for base in other_bases]
|
|
return _c3_merge(
|
|
[[cls]] +
|
|
explicit_c3_mros + abstract_c3_mros + other_c3_mros +
|
|
[explicit_bases] + [abstract_bases] + [other_bases]
|
|
)
|
|
|
|
def _compose_mro(cls, types):
|
|
"""Calculates the method resolution order for a given class *cls*.
|
|
|
|
Includes relevant abstract base classes (with their respective bases) from
|
|
the *types* iterable. Uses a modified C3 linearization algorithm.
|
|
|
|
"""
|
|
bases = set(cls.__mro__)
|
|
# Remove entries which are already present in the __mro__ or unrelated.
|
|
def is_related(typ):
|
|
return (typ not in bases and hasattr(typ, '__mro__')
|
|
and issubclass(cls, typ))
|
|
types = [n for n in types if is_related(n)]
|
|
# Remove entries which are strict bases of other entries (they will end up
|
|
# in the MRO anyway.
|
|
def is_strict_base(typ):
|
|
for other in types:
|
|
if typ != other and typ in other.__mro__:
|
|
return True
|
|
return False
|
|
types = [n for n in types if not is_strict_base(n)]
|
|
# Subclasses of the ABCs in *types* which are also implemented by
|
|
# *cls* can be used to stabilize ABC ordering.
|
|
type_set = set(types)
|
|
mro = []
|
|
for typ in types:
|
|
found = []
|
|
for sub in typ.__subclasses__():
|
|
if sub not in bases and issubclass(cls, sub):
|
|
found.append([s for s in sub.__mro__ if s in type_set])
|
|
if not found:
|
|
mro.append(typ)
|
|
continue
|
|
# Favor subclasses with the biggest number of useful bases
|
|
found.sort(key=len, reverse=True)
|
|
for sub in found:
|
|
for subcls in sub:
|
|
if subcls not in mro:
|
|
mro.append(subcls)
|
|
return _c3_mro(cls, abcs=mro)
|
|
|
|
def _find_impl(cls, registry):
|
|
"""Returns the best matching implementation from *registry* for type *cls*.
|
|
|
|
Where there is no registered implementation for a specific type, its method
|
|
resolution order is used to find a more generic implementation.
|
|
|
|
Note: if *registry* does not contain an implementation for the base
|
|
*object* type, this function may return None.
|
|
|
|
"""
|
|
mro = _compose_mro(cls, registry.keys())
|
|
match = None
|
|
for t in mro:
|
|
if match is not None:
|
|
# If *match* is an implicit ABC but there is another unrelated,
|
|
# equally matching implicit ABC, refuse the temptation to guess.
|
|
if (t in registry and t not in cls.__mro__
|
|
and match not in cls.__mro__
|
|
and not issubclass(match, t)):
|
|
raise RuntimeError("Ambiguous dispatch: {} or {}".format(
|
|
match, t))
|
|
break
|
|
if t in registry:
|
|
match = t
|
|
return registry.get(match)
|
|
|
|
def singledispatch(func):
|
|
"""Single-dispatch generic function decorator.
|
|
|
|
Transforms a function into a generic function, which can have different
|
|
behaviours depending upon the type of its first argument. The decorated
|
|
function acts as the default implementation, and additional
|
|
implementations can be registered using the register() attribute of the
|
|
generic function.
|
|
|
|
"""
|
|
registry = {}
|
|
dispatch_cache = WeakKeyDictionary()
|
|
cache_token = None
|
|
|
|
def dispatch(cls):
|
|
"""generic_func.dispatch(cls) -> <function implementation>
|
|
|
|
Runs the dispatch algorithm to return the best available implementation
|
|
for the given *cls* registered on *generic_func*.
|
|
|
|
"""
|
|
nonlocal cache_token
|
|
if cache_token is not None:
|
|
current_token = get_cache_token()
|
|
if cache_token != current_token:
|
|
dispatch_cache.clear()
|
|
cache_token = current_token
|
|
try:
|
|
impl = dispatch_cache[cls]
|
|
except KeyError:
|
|
try:
|
|
impl = registry[cls]
|
|
except KeyError:
|
|
impl = _find_impl(cls, registry)
|
|
dispatch_cache[cls] = impl
|
|
return impl
|
|
|
|
def register(cls, func=None):
|
|
"""generic_func.register(cls, func) -> func
|
|
|
|
Registers a new implementation for the given *cls* on a *generic_func*.
|
|
|
|
"""
|
|
nonlocal cache_token
|
|
if func is None:
|
|
return lambda f: register(cls, f)
|
|
registry[cls] = func
|
|
if cache_token is None and hasattr(cls, '__abstractmethods__'):
|
|
cache_token = get_cache_token()
|
|
dispatch_cache.clear()
|
|
return func
|
|
|
|
def wrapper(*args, **kw):
|
|
return dispatch(args[0].__class__)(*args, **kw)
|
|
|
|
registry[object] = func
|
|
wrapper.register = register
|
|
wrapper.dispatch = dispatch
|
|
wrapper.registry = MappingProxyType(registry)
|
|
wrapper._clear_cache = dispatch_cache.clear
|
|
update_wrapper(wrapper, func)
|
|
return wrapper
|